// verification is empty or invalid.
var errBitrotHashAlgoInvalid = StorageErr("bit-rot hash algorithm is invalid")

// errCrossDeviceLink - rename across devices not allowed.
var errCrossDeviceLink = StorageErr("Rename across devices not allowed, please fix your backend configuration")

// errLessData - returned when less data available than what was requested.

//
// Tensors may be copied on or off the device explicitly using
// TPUReplicatedInput and TPUReplicatedOutput respectively. For example, with
// two component devices, running `x = TPUReplicatedInput(inputs=[a, b])` on the
// parallel device creates a parallel tensor `x` with `a` on the first of
// `underlying_devices` and `b` on the second. Running `a_unpacked, b_unpacked =

...
BUILD SUCCESSFUL in 1m 30s
63 actionable tasks: 61 executed, 2 up-to-date

```

#include "tensorflow/core/ir/types/dialect.h"
#include "tensorflow/core/util/device_name_utils.h"

namespace mlir {
namespace TF {

using GpuDeviceMetadata = tf_type::GpuDeviceMetadataAttr;

// Tensorflow devices available at runtime with corresponding metadata if it is
// available. It's completely valid to have a device without any metadata
// attached to it.
class RuntimeDevices {

	// UpdateQOSCgroups performs housekeeping updates to ensure that the top
	// level QoS containers have their desired state in a thread-safe way
	UpdateQOSCgroups() error

	// GetResources returns RunContainerOptions with devices, mounts, and env fields populated for
	// extended resources required by container.
	GetResources(pod *v1.Pod, container *v1.Container) (*kubecontainer.RunContainerOptions, error)

      TFE_ContextListDevices(context.get(), status.get()), TF_DeleteDeviceList);
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  bool has_tpu = false;
  for (int device_index = 0; device_index < TF_DeviceListCount(devices.get());
       ++device_index) {
    std::string device_type =
        TF_DeviceListType(devices.get(), device_index, status.get());

// RUN: tf-opt %s -tf-replicated-clustering-bridge-v1 | FileCheck %s

module attributes {tf.devices = ["/job:localhost/replica:0/task:0/device:CPU:0", "/job:localhost/replica:0/task:0/device:TPU:0", "/job:localhost/replica:0/task:0/device:TPU:1", "/job:localhost/replica:0/task:0/device:TPU_SYSTEM:0"], tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 296 : i32}} {
  func.func @main() {
// CHECK: arith.constant
// CHECK: TPUCompile
// CHECK: TPUExecute

#include "tensorflow/core/lib/core/status.h"

namespace tensorflow {
namespace tf2xla {
namespace v2 {

// Run all the passes involved in transforming the graph before execution so
// that it is suitable for targeting devices when called with the TF 2 Function
// API. Users that need clustering with the Session API should use the v1 Bridge
// API. These transformations take as input a Tensorflow Graph as an MLIR Module

		case "ios":
			if isCorelliumBuilder(b) {
				// The corellium environment is self-hosting, so it should be able
				// to build even though real "ios" devices can't exec.
			} else {
				// The usual iOS sandbox does not allow the app to start another
				// process. If we add builders on stock iOS devices, they presumably
				// will not be able to exec, so we may as well allow that now.
				t.Logf("HasGoBuild is false on %s", b)
				return
			}

// so there is no risk of polluting the entire cache with data accessed once.
// Another goal of DirectIO is to minimize the mutation of data by the kernel
// before issuing IO to underlying devices. ZFS users often enable features like
// compression and checksumming which currently necessitates mutating data in
// the kernel.
//
// DirectIO semantics for a filesystem like ZFS would be quite different than